带权分块压缩感知的预测目标跟踪算法

针对矩形跟踪框在边缘处包含较多背景信息的问题,该文提出一种基于规范化梯度特征的带权分块压缩感知的目标特征提取方法。该方法将压缩感知测量矩阵转化为分块对角矩阵,且根据块的重要程度分配适当的权重,缩小测量矩阵规模,简化特征提取运算,弱化背景干扰。然后将提取的特征输入变先验概率的贝叶斯分类器,变先验概率的分类器充分利用已有的跟踪结果,从一定程度预测了目标的运动方向,减小候选目标的分类歧义性,使得每一帧的分类函数根据以往跟踪结果进行变化,提高了分类的准确度。实验在8个具有常见跟踪难度的序列中测试,并与目前较流行的4种目标跟踪算法在跟踪效果、成功率等方面进行比较,结果从多个角度表明,该文提出的目标跟踪算法具有较高的准确度和稳定性。     

基于近似观测的加权L1 压缩感知SAR成像

近些年,压缩感知(CS)理论已经被应用于合成孔径雷达(SAR)成像。传统的CS-SAR 成像需要消耗很高的计算机内存,为了减少计算机内存消耗,基于近似观测的CS-SAR 成像模型被提出。已有的基于近似观测的CS-SAR 成像模型使用Lq(0≤q≤1)正则化项来稀疏约束成像结果,当q 越小时,得到的解越稀疏;但是当q = 0 时, 该优化问题就变成了NP 难问题。提出了基于近似观测的加权L1 -CS-SAR 成像模型,加权L1 正则化既能够很好地逼 近L0 正则化,又能够避免NP 难问题。进一步地,我们针对该成像模型提出了相应的迭代加权阈值算法,仿真结果证明了所提出的成像算法的性能优于已存在的迭代阈值算法。     

基于非局部低秩和加权全变分的图像压缩感知重构算法

为准确有效地实现自然图像的压缩感知(CS)重构,该文提出一种基于图像非局部低秩(NLR)和加权全变分(WTV)的CS重构算法。该算法考虑图像的非局部自相似性(NSS)和局部光滑特性,对传统的全变分(TV)模型进行改进,只对图像的高频分量设置权重,并用一种差分曲率的边缘检测算子来构造权重系数。此外,算法以改进的TV模型与NLR模型为约束构建优化模型,并分别采用光滑非凸函数和软阈值函数来求解低秩和全变分优化问题,很好地利用了图像的自身性质,保护了图像的细节信息,并提高了算法的抗噪性和适应性。仿真结果表明,与基于NLR的CS算法相比,相同采样率下,该文所提算法的峰值信噪比最高可提高2.49 dB,且抗噪性更强,验证了算法的有效性。     

利用非线性约束整数最小二乘快速求解整周模糊度

整周模糊度的解算是高精度北斗姿态测量的核心问题,本文提出一种利用二次特征值求解非线性约束的最小二乘问题,用于整周模糊度快速求解,并将其应用在单频单历元的北斗姿态测量系统中.该方法将基线长度的先验信息与整周模糊度目标函数整合,构造新的目标函数,提高姿态测量中整周模糊度求解准确度.实验在北斗姿态测量平台上验证该方法的有效性,测试结果表明该方法在北斗单频单历元下,整周模糊度及姿态角求解的成功率有较大提升.     

多层叠加LDPC码编码调制技术

 本文提出了一种多层叠加LDPC码编码调制系统.与传统的基于速率分配的多层编码调制技术相比,多层叠加编码调制系统具有很好的对称性和可扩展性.通过分析比较Turbo码译码算法与LDPC码的译码算法的复杂度,本文指出了多层叠加LDPC码编码调制系统具有译码简单,易于实现的优点.实验结果表明,多层叠加LDPC码编码调制系统可以在不牺牲带宽的同时获得较好的性能.     

Compressed Sensing and Redundant Dictionaries

This paper extends the concept of compressed sensing to signals that are not sparse in an orthonormal basis but rather in a redundant dictionary. It is shown that a matrix, which is a composition of a random matrix of certain type and a deterministic dictionary, has small restricted isometry constants. Thus, signals that are sparse with respect to the dictionary can be recovered via basis pursuit (BP) from a small number of random measurements. Further, thresholding is investigated as recovery algorithm for compressed sensing, and conditions are provided that guarantee reconstruction with high probability. The different schemes are compared by numerical experiments.     

压缩感知的发展与应用

压缩感知(Compressed Sensing,CS)是近年来新兴的一种信号获取技术.沿着CS理论的发展历程介绍了CS理论框架,给出了其严格的数学描述,着重讨论了对原始信号的重构技术,最后介绍了一些可能的应用.     

Constrained Codes as Networks of Relations

We address the well-known problem of determining the capacity of constrained coding systems. While the one-dimensional case is well understood to the extent that there are techniques for rigorously deriving the exact capacity, in contrast, computing the exact capacity of a two-dimensional constrained coding system is still an elusive research challenge. The only known exception in the two-dimensional case is an exact (however, not rigorous) solution to the -run-length limited (RLL) system on the hexagonal lattice. Furthermore, only exponential-time algorithms are known for the related problem of counting the exact number of constrained two-dimensional information arrays. We present the first known rigorous technique that yields an exact capacity of a two-dimensional constrained coding system. In addition, we devise an efficient (polynomial time) algorithm for counting the exact number of constrained arrays of any given size. Our approach is a composition of a number of ideas and techniques: describing the capacity problem as a solution to a counting problem in networks of relations, graph-theoretic tools originally developed in the field of statistical mechanics, techniques for efficiently simulating quantum circuits, as well as ideas from the theory related to the spectral distribution of Toeplitz matrices. Using our technique, we derive a closed-form solution to the capacity related to the Path-Cover constraint in a two-dimensional triangular array (the resulting calculated capacity is ). Path-Cover is a generalization of the well known one-dimensional -RLL constraint for which the capacity is known to be .     

基于压缩感知的遥感图像融合方法

压缩感知理论,以远低于奈奎斯特采样定理要求的速率对图像进行采样,可利用图像的部分信息重构原始图像,有效地减轻图像处理的计算复杂度,降低对硬件的要求。文中提出了一种基于压缩感知的遥感图像融合方法。在压缩域对多光谱和全光谱遥感图像进行了融合实验,并与传统的融合方法进行了比较,实验结果表明,文中方法在遥感图像融合上有着良好的性能。     

Doubly-Selective Channel Estimation Using Superimposed Training and Weighted First-Order Statistics

Doubly-selective channel estimation using superimposed training and complex exponential basis expansion model is considered. By taking a weighted averaging operation of the received data, a weighted first-order statistical estimator is proposed, where the time-varying channel estimation is reduced to the simple average-based solution of time-invariant coefficients and the dominant effect of information-induced interference on channel estimation can be suppressed. To further improve the estimation performance with a limited training power, a joint iterative channel estimation and symbol detection scheme is developed where the detected symbol is exploited to enhance estimation performance instead of being viewed as interference. Theoretical analysis and simulation results show that the proposed scheme is superior to data-dependent superimposed training scheme and competitive with the conventional time-multiplexed training in terms of symbol error rate over doubly-selective channels.     

Optimized Projections for Compressed Sensing

Compressed sensing (CS) offers a joint compression and sensing processes, based on the existence of a sparse representation of the treated signal and a set of projected measurements. Work on CS thus far typically assumes that the projections are drawn at random. In this paper, we consider the optimization of these projections. Since such a direct optimization is prohibitive, we target an average measure of the mutual coherence of the effective dictionary, and demonstrate that this leads to better CS reconstruction performance. Both the basis pursuit (BP) and the orthogonal matching pursuit (OMP) are shown to benefit from the newly designed projections, with a reduction of the error rate by a factor of 10 and beyond.     

基于主分量分析的语音信号压缩感知

压缩感知理论是近年来兴起的一个新的研究热点。寻求适合于语音信号的稀疏基是压缩感知理论应用到语音信号处理领域的前提。本文基于主分量分析理论和大量的块数据,提取语音信号的特征信息,并根据压缩感知理论、字典构造的方法以及语音信号的特点,构造出一种新的适合于语音信号稀疏表示的冗余字典。该冗余字典是由多个正交基级联而成。为了更为客观的说明这种稀疏表示的优势,采用两种稀疏度的衡量标准来分别比较语音信号在DCT基、GABOR基和该冗余字典下的稀疏性,并且分别对男女声语音信号和清浊音进行了分析。实验表明,无论是男声信号还是女声信号,清音还是浊音,在该冗余字典下的稀疏性均优于DCT基,略差于GABOR基,但是由于其原子数远少于GABOR基,其计算的复杂度和存储量均低于GABOR基,因而比GABOR基更具可用性。      

压缩传感方位估计

信源方位估计是阵列信号处理的一个重要问题。基于信源空间分布稀疏的本质,利用压缩传感理论,构造出一种稀疏信源方位估计模型,仿真结果表明,在不考虑噪声的理想情况和满足压缩传感的条件下,不仅可以准确的恢复出原始信源的方位,而且精确的得到各个信源信号的强度,并且,这种新的模型只需要一次时间采样,从而大大降低了成本。     

压缩感知测量方法的机密性

分析了压缩感知（CS）的安全性问题,讨论了在攻击者不知道测量矩阵情况下是否 可以有效对信号进行重构的问题,论证了压缩感知可以达到保密性但达不到完善的保密性 。最后联合信道容量和速率失真函数,讨论了压缩感知恢复信号所需测量数据量的下限,并 分析了测量噪声对信号重构性能的影响。     

帧间自适应语音信号压缩感知

近年来提出的压缩感知是一种以低于传统奈奎斯特速率对信号采样可得到精确恢复的理论。该理论很快应用于简化传统的采样硬件、缩短采样时间、以及减少数据的存储空间。针对语音信号的传输问题,本文提出一种帧间自适应语音信号压缩感知的方法。在离散余弦变换域的语音信号具有稀疏性的前提下,以大量语音信号帧的分析统计为依据,提出一种基于语音帧能量分级和帧间位置惯性的语音信号自适应压缩感知算法。实验结果表明,能量自适应可以显著地提高语音信号的恢复质量,而位置自适应可以明显地减少语音信号的恢复时间,从而本文提出的算法可以用较少的恢复时间获得较好的恢复效果。      

Construction of Constrained Codes for State-Independent Decoding

Constrained sequence codes are widely used to meet constraints imposed by digital storage and communication systems. This paper develops an algorithm for the construction of constrained codes that admit state-independent decoding. By partitioning the code into a group of alphabets, one for each state, a codebook is developed using this algorithm that will allow the code to be decoded at the receiver without the need for state information. Finally, we use this algorithm to construct DCfree runlength-limited (RLL) codes, and we present two highly efficient state-independent decodable DC-free RLL codes.     

基于分布式压缩感知的遥感图像融合算法

针对基于压缩感知(Compressed Sensing, CS)理论的传统遥感图像融合算法未能考虑源图像信息相关性的特点,该文提出一种基于分布式压缩感知(Distributed Compressed Sensing, DCS)的遥感图像融合改进算法。通过DCS的第1联合稀疏模型(Joint Sparsity Model-1, JSM-1)提取源图像低频信息的公共部分和独有部分,再利用独有特征添加(UFA)的融合规则进行融合,从而提高融合精度。选取QuickBird卫星实测图像数据对该文方法和多个传统融合方法进行仿真实验并进行评价指标的对比,结果表明该文方法融合性能相对传统遥感图像融合方法都有不同程度的提高。     

多分辨率压缩感知技术软件仿真与分析

压缩感知理论(Compressed Sensing,CS)为宽带信号的直接信息采样(Analog-to-Information,A-to-I)提供了解决方法。针对现有的CS压缩算法没有充分考虑信息的重要性差异,运用仿真手段验证了多分辨率压缩感知(Multiresolution CS,MCS)思想的正确性,仿真结果表明:MCS能够动态地保护信号中蕴含的重要信息,且与单一分辨率CS重构算法比较,精度明显提高。为下一步将MCS为直接信息采样提供软件支持。     

基于压缩感知解码的网络编码技术

证明了随机网络编码的传输矩阵具有压缩感知观测矩阵的性质,提出了一种基于随机编码和压缩感知的网络编码方案,该方案利用了压缩感知求解欠定方程组的优点。在该方案中,随机网络编码的误码率可以依靠信息的稀疏性而降低。如果信息足够稀疏,那么网络的容量甚至可以超过限定了网络编码容量上界的最大流-最小割定理的理论值。给出了误码率的理论上界,仿真结果表明该方案可以达到较好的性能。